US8160732B2 - Noise suppressing method and noise suppressing apparatus - Google Patents

Noise suppressing method and noise suppressing apparatus Download PDF

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US8160732B2
US8160732B2 US11/914,550 US91455006A US8160732B2 US 8160732 B2 US8160732 B2 US 8160732B2 US 91455006 A US91455006 A US 91455006A US 8160732 B2 US8160732 B2 US 8160732B2
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spectrum
signal
noise
time
length
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Michiko Kazama
Mikio Tohyama
Koji Kushida
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Waseda University
Yamaha Corp
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering

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  • the present invention relates to a method and apparatus for suppressing noise by a spectrum subtraction method, which are increased in noise suppression performance.
  • the spectrum subtraction method is one of various techniques for suppressing noise that is included in a sound.
  • the spectrum subtraction method determines a spectrum of an observation signal in which noise is superimposed on a sound (hereinafter referred to as “observation signal spectrum”), estimates a spectrum of noise (hereinafter referred to as “noise spectrum”) from the observation signal spectrum, and obtains a spectrum of a noise-suppressed sound (hereinafter referred to as “sound spectrum”) by subtracting the noise spectrum from the observation signal spectrum.
  • the spectrum subtraction method then produces a noise-suppressed sound by converting the sound spectrum into a signal in the time domain.
  • a common observation signal spectrum is used as an observation signal spectrum used for estimation-calculating a noise spectrum (hereinafter referred to as “noise estimation spectrum”) and as an observation signal spectrum as a minuend from which to subtract the noise spectrum (hereinafter referred to as “noise suppression spectrum”).
  • Noise as a subject of suppression of the spectrum subtraction method is noise that does not vary much in time, such as stationary noise. Therefore, as long as the noise estimation spectrum is concerned, the frequency resolution is more important than the time resolution.
  • a sound as a subject of extraction of the spectrum subtraction method is a signal that varies much in time. Therefore, as long as the noise suppression spectrum is concerned, it is important that the time resolution be high.
  • the conventional spectrum subtraction method cannot satisfy both of frequency resolution that is necessary for the noise estimation spectrum and time resolution that is necessary for the noise suppression spectrum. As such, the conventional spectrum subtraction method is not sufficiently high in noise suppression performance.
  • the present invention has been made in view of the above points, and an object of the invention is therefore to provide a noise suppression method and a noise suppression apparatus which satisfy both of frequency resolution that is necessary for a noise estimation spectrum and time resolution that is necessary for a noise suppression spectrum and hence is increased in noise suppression performance.
  • This noise suppressing method can increase the frequency resolution that is necessary for a noise estimation spectrum, because the signal length of an observation signal that is extracted to analyze its spectrum to be used for estimation-calculating a noise spectrum is set relatively long. Furthermore, the noise suppressing method can increase the time resolution that is necessary for a noise suppression spectrum, because the signal length of an observation signal that is extracted to analyze its spectrum as a minuend from which to subtract a noise spectrum is set relatively short. As a result, both of frequency resolution that is necessary for a noise estimation spectrum and time resolution that is necessary for a noise suppression spectrum can be satisfied and hence the noise suppression performance can be increased.
  • a noise suppressing method comprises the steps of extracting a part an observation signal that progresses with time and in which noise is superimposed on a sound, every time a prescribed interval of time with which the observation signal progresses elapses, in a first signal length that is longer than or equal to the prescribed time interval; analyzing, as a first spectrum, a spectrum of the observation signal that has been extracted in the first signal length; extracting a part of the observation signal every time the prescribed time interval or a proper time elapses in a second signal length that is longer than the first signal length in such a manner that its head coincides with a head of the observation signal that is extracted in the first signal length; analyzing, as a second spectrum, a spectrum of the observation signal that has been extracted in the second signal length; estimation-calculating a spectrum of noise included in the observation signal on the basis of the second spectrum; subtracting the noise spectrum from the first spectrum every time the prescribed time interval elapses, to calculate a noise-suppressed sound
  • This noise suppressing method comprises the steps of smoothing-processing the second spectrum, and estimation-calculating a noise spectrum on the basis of a smoothing-processed second spectrum.
  • the subtracting step is executed after the estimated noise spectrum is subjected to smoothing processing.
  • the smoothing processing the substantial frequency resolution of the noise spectrum is made equal to (or close to) that of the first spectrum.
  • the estimation-calculating step comprises the substeps of smoothing-processing the second spectrum; comparing a smoothing-processed second spectrum with the second spectrum that has not been smoothing-processed; choosing larger values at respective frequency points in the comparing substep, to eliminate dips in the second spectrum; and estimation-calculating a noise spectrum on the basis of a dip-eliminated second spectrum.
  • the subtracting step comprises the substeps of smoothing-processing the estimated noise spectrum; comparing a smoothing-processed noise spectrum with the noise spectrum that has not been smoothing-processed; choosing larger values at respective frequency points in the comparing substep, to eliminate dips in the noise spectrum; and subtracting a dip-eliminated noise spectrum from the first spectrum.
  • processing noise i.e., noise that is newly generated by signal processing; musical noise.
  • Occurrence of processing noise can be suppressed by estimation-calculating a noise spectrum after eliminating dips from the second spectrum or subtracting a noise spectrum from the first spectrum after eliminating dips from the noise spectrum.
  • the technique of eliminating dips from a noise spectrum or an observation signal spectrum to be used for estimation-calculating a noise spectrum can be applied to not only the case that the signal length of an observation signal that is extracted to analyze an observation signal spectrum to be used for estimation-calculating a noise spectrum is set longer than the signal length of an observation signal that is extracted to analyze an observation signal spectrum as a minuend from which to subtract a noise spectrum, but also a case that the two kinds of signal length are set identical.
  • the above noise suppressing method comprises the steps of adding a zero signal having a prescribed length after an end of the observation signal that has been extracted in the first signal length so that a signal length of the observation signal to be used for the analysis of the first spectrum is made equal to the second signal length; analyzing, as a first spectrum, a spectrum of the observation signal to which the zero signal is added; subtracting the noise spectrum from the analyzed first spectrum; converting a sound spectrum that has been obtained by the subtracting step into a signal in the time domain; removing a signal having the same length as the added zero signal located after an end of the time-domain signal, to return a signal length of the time-domain signal to the first signal length; and connecting the time-domain signals to each other whose signal length is returned to the first signal length.
  • the prescribed time interval may be, for example, a half of the first signal length.
  • the noise suppressing method may be such that the time-domain signal is a signal that is obtained in the first signal length every time the prescribed time interval elapses, and that the time-domain signal is multiplied by a triangular window and the time-domain signals that have been multiplied by the triangular window are added to each other sequentially and thereby connected to each other.
  • a noise suppressing apparatus which is a more specific version, comprises a first signal extracting section for extracting a part an observation signal that progresses with time and in which noise is superimposed on a sound, every time a prescribed interval of time with which the observation signal progresses elapses, in a first signal length that is longer than or equal to the prescribed time interval; a first spectrum analyzing section for analyzing, as a first spectrum, a spectrum of the observation signal that has been extracted by the first signal extracting section; a second extracting section for extracting a part of the observation signal every time the prescribed time interval or a proper time elapses in a second signal length that is longer than the first signal length in such a manner that its head coincides with a head of the observation signal that is extracted in the first signal length; a second spectrum analyzing section for analyzing, as a second spectrum, a spectrum of the observation signal that has been extracted by the second signal extracting section; a noise spectrum estimation-calculating section for estimation-calculating a spectrum of noise included in
  • FIG. 1 is a flowchart outlining the procedure of a noise suppressing process which utilizes a noise suppression method according to the invention.
  • FIG. 2 is an explanatory diagram of the noise suppressing process.
  • FIG. 3 shows functional blocks of an embodiment of a noise suppressing apparatus for executing the noise suppressing process of FIG. 1 .
  • FIG. 4 is a spectrum diagram showing the operation of a dip eliminating section 22 shown in FIG. 2 .
  • FIG. 5 is a block diagram showing specific examples of a noise estimating section 28 and a suppression calculating section 40 .
  • FIG. 6 is a waveform diagram showing differences between output waveforms that were obtained when stationary noise was input in a conventional spectrum subtraction method and the spectrum subtraction method according to the invention.
  • FIG. 7 is a waveform diagram of a case that a sound with noise is input to the noise suppressing apparatus according to the invention.
  • FIG. 1 outlines the procedure of a noise suppressing process which utilizes a noise suppression method according to the invention.
  • FIG. 2 is an explanatory diagram of the noise suppressing process.
  • noise e.g., an audio signal received through a telephone communication or a signal that is input for speech recognition
  • the observation signal x 0 (n) is subjected to frame extracting (signal extracting) in different frame lengths (signal lengths, time window lengths) for analysis of a noise suppression spectrum and for analysis of a noise suppression spectrum (S 1 and S 2 ).
  • frames for analysis of a noise suppression spectrum are extracted from the observation signal x 0 (n) in a relatively short frame length T 1 (S 1 ; the relatively short frame length T 1 and frames that are extracted from the observation signal x 0 (n) in this frame length will be hereinafter referred to as “noise suppression frame length” and “noise suppression frames,” respectively) and frames for analysis of a noise estimation spectrum are extracted from the observation signal x 0 (n) in a relatively great length T 2 (S 2 ; the relatively great frame length T 2 and frames that are extracted from the observation signal x 0 (n) in this frame length will be hereinafter referred to as “noise estimation frame length” and “noise estimation frames,” respectively).
  • a noise suppression frame and a noise estimation frame are extracted from the observation signal (S 1 and S 2 ) repeatedly, that is, every time a half of the noise suppression frame length T 1 elapses, in such a manner that the heads of the noise suppression frame and the noise estimation frame are timed with each other (i.e., observation signal samples (latest samples) of the same time point are located at the heads of the two frames).
  • Zero data having a prescribed length i.e., sample data whose signal values are zero, a zero signal
  • the frame length is made equal to the noise estimation frame length T 2 formally (in a simulated manner) (S 3 ).
  • This processing is performed because to subtract a noise spectrum from a noise suppression spectrum it is necessary that the numbers of data (the numbers of frequency points) of the two spectra be the same. That is, the number of data of the noise spectrum is the same as that of a noise estimation spectrum, and to equalize the number of data of the noise suppression spectrum to that of the noise estimation spectrum it is necessary to equalize the numbers of data (the numbers of samples) of the noise suppression spectrum and the noise estimation spectrum in the time domain before conversion into data in the frequency domain.
  • the noise suppression frame length T 1 can be set at 20 to 32 ms, for example.
  • the noise estimation frame length T 2 can be set about eight times longer than the noise suppression frame length T 1 (e.g., 256 ms).
  • “(a) Process before noise suppression” is the above-described steps S 1 -S 3 . More specifically, every time M/2 samples of an observation signal is newly input (every time T1 ⁇ 2 elapses), latest M samples of the observation signal are extracted as a noise suppression frame (i.e., noise suppression frames are extracted with an overlap of M/2 samples) and latest N samples (N>M; in FIG. 2 , N is set equal to 8M) of the observation signal are extracted as a noise estimation frame. Zero data of (N ⁇ M) samples are added after the end of each noise suppression frame, whereby the frame length of each noise suppression frame is made equal to the noise estimation frame length T 2 formally.
  • every time the data of a noise suppression frame are extracted i.e., for each time interval corresponding to M/2 samples of the observation signal
  • the data of the noise suppression frame to which zero data are added are subjected to fast Fourier transform (FFT) and thereby converted into data in the frequency domain, that is, a noise suppression spectrum X 1 (k) (S 4 ).
  • FFT fast Fourier transform
  • every time the data of a noise estimation frame is extracted i.e., for each time interval corresponding to M/2 samples of the observation signal
  • the data of the noise estimation frame is subjected to fast Fourier transform and thereby converted into a signal in the frequency domain, that is, a noise estimation spectrum X 2 (k) (S 5 ).
  • a noise estimation spectrum X 2 (k) is calculated (i.e., for each time interval corresponding to M/2 samples of the observation signal), the noise estimation spectrum X 2 (k) is subjected to proper dip elimination processing or smoothing processing (S 6 ). Every time the dip elimination processing or smoothing processing is performed (i.e., for each time interval corresponding to M/2 samples of the observation signal), an operation of estimating a current noise spectrum N(k) is performed on the basis of a noise estimation spectrum X 2 ′(k) produced by the dip elimination processing or smoothing processing and estimation values of a preceding noise spectrum (S 7 ).
  • noise suppression spectrum X 1 (k) and a noise spectrum N(k) are calculated (i.e., for each time interval corresponding to M/2 samples of the observation signal), the noise spectrum N(k) is subtracted from the noise suppression spectrum X 1 (k), whereby a noise-suppressed sound spectrum G(k) is calculated (S 8 ).
  • the sound spectrum G(k) is subjected to inverse fast Fourier transform (I-FFT) and thereby converted into a signal in the time domain, that is, an audio signal (S 9 ).
  • I-FFT inverse fast Fourier transform
  • Audio signals of frames that are obtained at the time intervals of M/2 samples of the observation signal are connected to each other (S 10 ) and output as a continuous audio signal g(n), which will be output as a sound from a speaker device, used for speech recognition processing for the speaker, or used for some other purpose.
  • step S 10 “(b) Process after noise suppression” is step S 10 (frame combining). More specifically, (N ⁇ M) tail samples corresponding to the added zero data are removed from the frame of N samples obtained by the inverse fast Fourier transform (S 9 ), whereby a frame is obtained which has M samples as in the original state. The data of each of frames of M samples that are obtained at the time intervals of M/2 samples of the observation signal is multiplied by a triangular window (i.e., the data are given a gain characteristic that increases linearly from 0 to 1 in the first half frame of the one frame length (the time length of M samples) and decreases 1 to 0 In the second half frame). Resulting frames are added to each other with an overlap of a 1 ⁇ 2 frame, whereby a continuous audio signal is generated. As a result, a continuous audio signal is obtained which is free of disconnections or steps between the frames.
  • a triangular window i.e., the data are given a gain characteristic that increases linearly from 0 to 1 in the
  • N noise estimation frame length T 2 : 4,096 samples (corresponds to 256 ms)
  • FIG. 3 shows functional blocks of the noise suppressing apparatus.
  • An input signal (audio signal with noise) x 0 (n) is input to both of a noise spectrum output section 10 and a noise suppressing section 12 .
  • the audio signal with noise that is input to the noise spectrum output section 10 is first subjected to a frequency analysis for noise estimation in a noise estimation spectrum analyzing section 14 . More specifically, every time an input signal of M/2 samples (256 samples) is newly input, a frame extracting section 16 extracts an input signal of latest N (4,096) samples.
  • An amplitude spectrum calculating section 20 calculates an amplitude spectrum from the calculated spectrum data X 2 (k).
  • a dip eliminating section 22 eliminates dips in the frequency characteristic from the calculated amplitude spectrum.
  • the dip elimination processing is performed in the following manner.
  • the amplitude spectrum is subjected to smoothing processing in a smoothing processing section 24 .
  • the algorithm of the smoothing processing may be a moving average method, in which an amplitude value at the center of a prescribed number of consecutive frequency points (i.e., a prescribed frequency band) is replaced by an average of amplitude values at these frequency points.
  • the substantial frequency resolution of a smoothed amplitude spectrum becomes equal to that of a noise suppression amplitude spectrum.
  • the average calculation and the amplitude value replacement are performed while the frequency point is shifted by one point each time, whereby an amplitude spectrum is calculated that is smoothed over the entire frequency band.
  • a moving median method may be employed as an algorithm of the smoothing processing of the smoothing processing section 24 .
  • an amplitude value at the center of a prescribed number of (e.g., eight) consecutive frequency points (i.e., a prescribed frequency band) is replaced by a median of amplitude values at these frequency points.
  • the extraction of a median amplitude value and the amplitude value replacement are performed while the frequency point is shifted by one point each time, whereby an amplitude spectrum is calculated that is smoothed over the entire frequency band.
  • a comparing section 26 compares the amplitude spectrum that has been smoothed by the smoothing processing section 24 with the unsmoothed amplitude spectrum and thereby chooses larger values at respective frequency points.
  • the comparing section 26 thus outputs, as a noise estimation amplitude spectrum
  • is thus obtained.
  • FIG. 4 shows the operation of the dip eliminating section 22 (only part (frequency range: 1 to 100 Hz) of the entire amplitude spectrum is shown in an enlarged manner).
  • An unsmoothed amplitude spectrum A and an amplitude spectrum B that has been smoothed by the moving average method are compared with each other and larger values (indicated by dots) are chosen at respective frequency points.
  • a continuous characteristic that is a connection of the chosen values is output from the dip eliminating section 22 as a dip-eliminated amplitude spectrum.
  • dips (valleys) are removed from the amplitude spectrum A and processing noise is reduced.
  • the comparing section 26 shown in FIG. 3 may be omitted (i.e., only the smoothing processing section 24 is provided in place of the dip-eliminating section 22 ).
  • an output signal of the smoothing processing section 24 i.e., an amplitude spectrum that has been smoothed by the moving average method, the moving median method, or the like
  • the noise estimating section 28 estimation-calculates an amplitude spectrum of noise included in the observation signal (hereinafter referred to as “noise amplitude spectrum”) according to an arbitrary estimation algorithm on the basis of the dip-eliminated or smoothed amplitude spectrum.
  • the dip eliminating section 22 (or the smoothing processing section 24 that replaces the dip eliminating section 22 ) may be disposed downstream of the noise estimating section 28 rather than upstream of it.
  • a suppression spectrum analyzing section 30 the input signal (audio signal with noise) x 0 (n) that is input to the noise suppressing section 12 is first subjected to a frequency analysis for noise suppression (i.e., for generation of an observation signal spectrum as a minuend from which to subtract a noise spectrum). More specifically, every time an input signal of M/2 samples (256 samples) is newly input, a frame extracting section 32 extracts an input signal of latest M (512) samples. A zero data generating section 34 generates zero data of (N ⁇ M) samples (3,584 samples).
  • An adding section 36 adds the zero data of (N ⁇ M) samples after the end of the input signal of M samples that has been extracted by the frame extracting section 32 , and thereby equalizes the length of the extracted input signal to the noise estimation frame length T 2 formally.
  • a suppression calculating section 40 performs noise suppression processing according to an arbitrary suppression algorithm on the basis of the noise suppression spectrum X 1 (k) that is output from the suppression spectrum analyzing section 30 and the noise amplitude spectrum
  • a noise-suppressed sound spectrum G(k) that is output from the suppression calculating section 40 is subjected to inverse fast Fourier transform in an inverse fast Fourier transform section 42 and thereby returned to a signal in the time domain.
  • the signal that is output from the inverse fast Fourier transform section 42 is data of N (4,096) samples
  • the lower (N ⁇ M) samples (3,584 samples) corresponding to the zero data are removed from the signal by an output combining section 44 , whereby data of M (512) samples (i.e., samples of the original number) are obtained.
  • Frames are connected to each other, whereby a continuous audio signal g(n) is output.
  • FIG. 5 shows specific examples of the noise estimating section 28 and the suppression calculating section 40 .
  • a spectrum envelope extracting section 45 extracts an envelope
  • an average spectrum of noise has a smooth distribution that is almost uniform over a wide band if the average spectrum is obtained by repeating observations for a long time.
  • a spectrum of noise has a variation (peaks and valleys).
  • a frequency characteristic of a sound has large amplitude values in particular frequency bands and is not uniform over the entire frequency band.
  • a noise spectrum is estimated by discriminating noise that is distributed uniformly over the entire frequency band and a sound having large amplitude values in particular frequency bands using the magnitude of a spectrum correlation value. Therefore, fine peak/valley characteristics of the noise amplitude spectrum are eliminated.
  • the spectrum envelope extracting section 45 extracts an envelope by performing lowpass filter processing on the noise estimation amplitude spectrum
  • the lowpass filter processing may be such that the noise estimation amplitude spectrum
  • by the spectrum envelope extracting section 45 is such that the noise estimation amplitude spectrum
  • a noise amplitude spectrum initial value output section 46 outputs initial values of a noise amplitude spectrum. That is, initial values are set because immediately after activation of this apparatus there are no noise amplitude spectrum data to be referred to. Examples of the method for setting noise amplitude spectrum initial values are as follows:
  • Method 1 Data of only background noise (i.e., mixed with no sound), which are input immediately after activation, are subjected to Fourier transform, and amplitude spectrum data calculated from Fourier-transformed data are set as noise amplitude spectrum initial values.
  • Amplitude spectrum data corresponding to background noise are held in a memory in advance, and read out and set as noise amplitude spectrum initial values at the time of activation.
  • envelope data of amplitude spectrum data corresponding to background noise are held in a memory in advance, and read out and set as initial values of noise amplitude spectrum envelope data at the time of activation.
  • a noise amplitude spectrum updating section 48 sequentially receives noise amplitude spectra
  • the noise amplitude spectrum updating section 48 delays the noise amplitude spectra
  • the noise amplitude spectrum updating section 48 outputs the noise amplitude spectrum initial values that are set by the noise amplitude spectrum initial value output section 46 .
  • a spectrum envelope extracting section 52 extracts an envelope
  • a correlation value calculating section 54 calculates a correlation value (correlation coefficient) ⁇ of the noise estimation amplitude spectrum envelope
  • written as
  • the noise amplitude spectrum calculating section 50 calculates a noise amplitude spectrum
  • [1 ⁇ l /(1+ ⁇ l ) ⁇ m ] ⁇
  • ⁇ the correlation value of the envelope of the audio signal spectrum of the frame being observed and the envelope of the noise spectrum that was estimated for the audio signal of the frame that was observed last time
  • Equation (2) is to estimate a new noise amplitude spectrum
  • is prevented from varying being influenced by the sound component.
  • the correlation value ⁇ is large, it is judged that the sound component is a minor part of the input signal (i.e., a silent interval). Therefore, addition is made in such a manner that the proportion of the noise amplitude spectrum
  • calculated this time are added together at an even ratio (0.5:0.5). In this manner, the noise amplitude spectrum is updated mainly in silent intervals.
  • Equation (2) the parameter l is a constant for adjusting the sensitivity to a small correlation value. The degree of updating of noise amplitude spectrum estimation values of low correlation becomes smaller as the l-value increases.
  • the parameter m is a constant for adjusting the degree of updating. The degree of updating decreases as the m-value increases.
  • the noise suppression spectrum X 1 (k) is input to an amplitude spectrum calculating section 56 and a phase spectrum calculating section 58 .
  • the amplitude spectrum calculating section 56 calculates an amplitude spectrum
  • ⁇ X R ( k ) 2 +X l ( k ) 2 ⁇ 1/2 (3)
  • X 1 (k) the imaginary part of X 1 (k).
  • a spectrum subtracting section 60 calculates a noise-amplitude-spectrum-eliminated amplitude spectrum
  • a recombining section 62 recombines the amplitude spectrum
  • of the audio signal of the current frame that has been calculated by the spectrum subtracting section 60 and the phase spectrum ⁇ (k) of the noise suppression spectrum X 1 (k) of the current frame that has been calculated by the phase spectrum calculating section 58 and thereby generates a complex spectrum given by the following Equation (6), that is, a noise-suppressed sound spectrum G(k): G ( k )
  • the generated sound spectrum G(k) is supplied to the inverse fast Fourier transform section 42 shown in FIG. 3 .
  • FIG. 6 shows output waveforms that were obtained when stationary noise was input to noise suppressing apparatus.
  • Symbol (a) denotes original noise.
  • Symbols (b) and (c) denote noise-suppressed outputs of a conventional spectrum subtraction method in which the length of frames extracted from an observation signal was common to the purposes of noise estimation and noise suppression.
  • the output (b) corresponds to a case that the extracting frame length was set at 32 ms
  • the output (c) corresponds to a case that the extracting frame length was set at 256 ms.
  • Symbols (d) and (e) denote noise-suppressed outputs of the noise suppressing method according to the invention in which the extracting frame length for noise estimation (T 2 ) and that for noise suppression (T 1 ) were set at 256 ms and 32 ms, respectively.
  • the output (d) corresponds to a case that the dip elimination processing of the dip eliminating section 22 (see FIG. 3 ) was not performed, and the output (c) corresponds to a case that the dip elimination processing was performed.
  • degrees of attenuation from the original noise (a) were
  • FIG. 7 is a waveform diagram of a case that a sound with noise is input to the noise suppressing apparatus according to the invention.
  • the noise estimation frame length T 2 is set at 256 ms and the noise suppression frame length T 1 is set at 32 ms.
  • Symbol (a) denotes a sound with noise.
  • Symbol (b) denotes a noise-suppressed output.
  • symbol (c) denotes suppressed (eliminated) noise. It is seen from FIG. 7 that the sound (b) is obtained by suppressing the stationary noise (c) in the sound (a) with noise.
  • the above embodiments employ the amplitude spectrum subtraction method in which a noise amplitude spectrum
  • a power spectrum subtraction method may be employed in which a noise power spectrum
  • the noise estimation processing is necessarily performed every prescribed time interval (every time T1 ⁇ 2 elapses), it may be performed every time a proper occasion arises.
  • a process may be employed in which intervals in which noise estimation can be performed easily such as silent intervals or faint sound intervals are detected in real time and the noise estimation processing is performed only in those intervals (i.e., the noise estimation processing is not performed (i.e., it is suspended) in the other intervals).
  • the noise estimation processing may be suspended in intervals with a small noise variation or intervals in which reduction in processing load is desired.
  • a process may be employed in which the data (noise amplitude spectrum
  • the time window length in which to extract an observation signal for noise suppression i.e., the noise suppression frame length T 1 , the period of M samples
  • the cutting time interval i.e., the period of M/2 samples

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